Traditional scorecards take a collection of input fields and produce a score to predict the likelihood of some event. Each input is binned according to the stated range of that bin. For a numeric field such as age, these bins are arranged consecutively. For a categorical field such as employment type, each category could be regarded as a bin in its own right, or several categories could be grouped together into a single bin. Each bin has an associated score. The scores for the selected bins for every field are summed to produce the overall score of the scorecard. An example of a traditional scorecard is shown in FIG. 1.
If a set of examples is available where the outcome is known, then analytical routines may be applied to generate the bin ranges and scores automatically. The outcome is encoded as a binary field to indicate either a positive or negative outcome. This then constitutes the target field for the analytical routines.
Neural techniques can use examples of inputs and targets to build models to estimate those targets. This model building proceeds iteratively by first initialising the model arbitrarily and then: presenting a number of examples, evaluating the model's resulting performance, altering the model to improve its performance, and then repeating this step until the required performance is achieved. This process is referred to as training the model. Model training converges to a solution that takes into account the whole problem including the interaction between fields and non-linear relationships between target and input. Many such neural models have been invented.
These aspects of neural models are worth utilising in a procedure for the automatic creation of traditional scorecards. However, it is difficult to apply neural training techniques to traditional scorecards because the bin boundaries make the traditional scorecard function discontinuous.
The present invention attempts to overcome these problems by approximating a traditional scorecard using a neural model.